Factors Influencing Consumer Behavior Regarding Online Health Information—An Exploratory Factor Analysis Approach

BACKGROUND Advance technological breakthroughs have dramatically contributed to the nature of the healthcare environment paired with current concerns arise over patients’ health information seeking and how to seek them effectively; the e-health systems are gaining the attention of more and more people. OBJECTIVE The primary objective of this study was to conduct exploratory factor analysis (EFA) to validate questionnaire instruments for measuring the consumers’ behavior in online health information-seeking via e-health websites. METHODS The questionnaire was refined and modified from sixteen different studies. Survey questionnaires were randomly distributed to consumers at public places (such as hospitals and universities) in Kuala Lumpur and Selangor. Over three weeks, 100 participants were recruited. EFA was performed using Principal Component Analysis as a variable reduction technique. RESULTS The results of the analysis showed that all of the twelve constructs consist of only one component or dimension. The value of factor loading for every item in each construct is more than 0.6, which represents that the factor extracts sufficient variance from that item. Kaiser-Meyer-Olkin Measure of Sampling Adequacy was higher than 0.6 for all the constructs, which demonstrate that the sample size is adequate. For all constructs, the P-value from Bartlett’s Test of Sphericity is lower than the significance level (<.05), which indicates that the dataset is suitable for a data reduction technique. For the entire constructs’ items, Cronbach’s Alpha value was higher than 0.7, which demonstrates the reliability of all items. CONCLUSIONS This study has established in developing a valid and reliable instrument for measuring online consumers’ health information-seeking behavior via e-health websites.

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Exploratory Factor Analysis Approach for Understanding Consumer Behavior toward Using Chongqing City Card

Advanced Data Mining and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-17313-4_58 ◽

2010 ◽

pp. 561-567 ◽

Cited By ~ 1

Author(s):

Juanjuan Chen ◽

Chengliang Wang

Keyword(s):

Factor Analysis ◽

Consumer Behavior ◽

Exploratory Factor Analysis ◽

Analysis Approach ◽

Chongqing City

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Factors Influencing Consumer Behavior and Prospective Purchase Decisions in a Dynamic Pricing Environment—An Exploratory Factor Analysis Approach

Social Sciences ◽

10.3390/socsci7090153 ◽

2018 ◽

Vol 7 (9) ◽

pp. 153 ◽

Cited By ~ 7

Author(s):

Vijay Victor ◽

Jose Joy Thoppan ◽

Robert Jeyakumar Nathan ◽

Fekete Farkas Maria

Keyword(s):

Factor Analysis ◽

Consumer Behavior ◽

Exploratory Factor Analysis ◽

Dynamic Pricing ◽

Industrial Revolution ◽

Analysis Approach ◽

Retail Sector ◽

Purchase Decisions ◽

Fair Price ◽

Late 20Th Century

The rapid advancements in information and communication technology during the third industrial revolution of the late 20th century has marked the beginning of a new era in the retail sector with the introduction of E-commerce. The dawn of the new century witnessed industry 4.0, revolutionizing all areas of online business by bringing in novel opportunities and possibilities. Despite the progress in technology, the determination of correct pricing on online selling platforms still remains a very complex task. The adoption of big data technology has enabled online sellers to make real-time price changes of high magnitude and proximity. However, with increasing awareness among buyers regarding modern pricing strategies, it is necessary to examine probable changes in consumer behavior when exposed to dynamic pricing scenarios. This study investigates the factors that influence consumer behavior, and their prospective online purchase decisions in a dynamic pricing context, through an exploratory factor analysis approach. A primary research survey was conducted, and 178 samples were finalized for data analysis through a series of web surveys completed by respondents in India. This study identifies, measures and classifies 27 research items into variables, namely shopping experience, privacy concerns, awareness about dynamic pricing, buying strategy, fair price perceptions, reprisal intentions and intentions for self-protection. These seven factors could be used to explain consumer behavior in a dynamic pricing situation.

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